1. Field of the Invention
The present invention generally relates to computer implemented planning resources and decision support tools and, more particularly, to a tool which provides the user a common data structure and architecture to execute various types of solvers which match assets with demands to support critical business processes in production planning and scheduling.
2. Background Description
Within the complexity of microelectronics and related manufacturing, four related decision areas or tiers can be distinguished based on the time scale of the planning horizon and the apparent width of the opportunity window. To facilitate an understanding of the four decision tiers in semiconductor manufacturing, consider the following oven example, with reference to FIG. 1 which is a diagram associated with this example.
Within a zone of control 10, there is a coater machine 12, a work-in-progress (WIP) queue 14, and an oven set 16. Wafers move around the zone of control in groups of twenty-five, called a lot. All wafers in the lot are the same type. Each lot must pass through the oven operation ten times. Each oven set is composed of four ovens or tubes 161, 162, 163, and 164 and one robot 166 to load and unload the oven. It takes about ten minutes to load or unload an oven. The process time in the oven depends on the iteration. We will assume one lot to an oven at a time. Before a wafer enters into the oven, it must be coated by the coater machine 12. The coating process takes twenty minutes. The coating expires in four hours. If the coating expires, the wafer must be stripped, cleaned, and then recoated. This process takes four hours and often generates yield losses.
The first decision tier, strategic scheduling, is driven by the time frame or lead time required for the business plan, resource acquisition, and new product introduction. This tier can often be viewed in two parts; very long-term and long-term. Here, decision makers are concerned with a set of problems that are three months to seven years into the future. Issues considered include, but are not limited to, what markets they will be in, general availability of tooling and workers, major changes in processes, changes in or risk assessment of demand for existing product, required or expected incremental improvements in the production process, lead times for additional tooling, manpower and planning. In the oven example of FIG. 1, very-long-term decisions are made on whether the ovens are necessary to the production process, and if so the characteristics needed in the ovens. Long-term decisions are made about how many ovens to buy. Tools typically used in planning of this scope are models for capacity planning, cost/pricing, investment optimization, and simulations of key business measures.
The second tier, tactical scheduling, deals with problems the company faces in the next week to six months. Estimates are made of yields, cycle times, and binning percentages. Permissible substitutions are identified. Decisions are made about scheduling starts or releases into the manufacturing line (committing available capacity to new starts). Delivery dates are estimated for firm orders, available "outs" by time buckets are estimated for bulk products, and daily going rates for schedule driven product are set. The order/release plan is generated/regenerated. Reschedules are negotiated with or requested by the ultimate customer. In the oven example of FIG. 1, typical decision areas would include the daily going rate for different products, the allocation of resources between operations, the number of operators to assign, and machine dedication. Tools typically used in the planning and scheduling of this phase are forward schedulers, fast capacity checkers, and optimization of capacity, commits and cost.
The third tier, operational scheduling, deals with the execution and achievement of a weekly plan. Shipments are made. Serviceability levels are measured. Recovery actions are taken. Optimized consumption of capacity and output of product computed. Tools typically used in support of daily activities are decision support, recovery models, prioritization techniques and deterministic forward schedulers. Manufacturing Execution Systems (MES) are used for floor communications and control. In the oven example of FIG. 1, priorities would be placed on each lot arriving at the ovens based on their relevance to the current plan or record. If the ovens "go down", their priority in the repair queue would be set by decisions made in this tier.
The fourth tier, dispatch scheduling or response system, addresses the problems of the next hour to a few weeks by responding to conditions as they emerge in real time and accommodates variances from availability assumed by systems in the plan creation and commitment phases. Essentially, they instruct the operator what to do next to achieve the current goals of manufacturing. Dispatch scheduling decisions concern monitoring and controlling of the actual manufacturing flow or logistics. Here, decisions are made concerning trade-offs between running test lots for a change in an existing product or a new product and running regular manufacturing lots, lot expiration, prioritizing late lots, positioning preventive maintenance downtime, production of similar problems to reduce setup time, downstream needs, simultaneous requests on the same piece of equipment, preferred machines for yield considerations, assigning personnel to machines, covering for absences, and reestablishing steady production flow after a machine has been down. In the oven example, the question should be which lot (if any) should be run next when an oven is free. Tools used are rule based dispatchers, short interval schedulers and mechanical work-in-progress (WIP) limiting constructions.
Of course, there is overlap and interaction between the four decision tiers, but typically different groups are responsible for different scheduling decisions. For example, maintenance may decide on training for their personnel, on work schedules for their people, preventive maintenance, and which machine to repair next. Finance and each building superintendent may make decisions on capital equipment purchases. Industrial Engineering may have the final say on total manpower, but a building superintendent may do the day-to-day scheduling. Marketing may decide when orders for products can be filled and what schedule commitments to make. For strategic and operational decisions, these groups and their associated decision support tools are loosely coordinated or coupled. Finance only requires an estimate of required new tools from each building to estimate capital purchase. Each building requires an estimate on new tool requirements from the product development people. Dispatch decisions must be tightly coupled. Lots only get processed when the appropriate tool, operator, and raw material are available. At dispatch rough estimates are no longer sufficient. If a machine is down maintenance must have the appropriately trained individual available to repair the machine. Manufacturing must have the appropriate mix of tools and workers to produce finished goods on a timely basis. At dispatch the decisions made by various groups must by in synchronization or nothing is produced. A manufacturing facility accommodates this tight coupling in only one of two ways; slack (extra tooling and manpower, long lead times, limited product variation, excess inventory and people, differential quality, brand loyalty, and so forth), or strong information systems to make effective decisions.
Within the first, second and third decision tiers, a major planning activity undertaken by microelectronic firms is matching assets with demands. This activity can be broken into three major types of matching that are used throughout microelectronics to support decision making:
(a) Materials Requirements Planning (MRP) type of matching--"Opportunity Identification" or "Wish list". For a given set of demand and a given asset profile what work needs to be accomplished to meet the demand. PA1 (B) Projected Supply Planning (PSP). Given a set of assets, manufacturing specifications, and business guidelines this application creates an expected or projected supply picture over the next "t" time units. The user supplies guidelines to direct how to flow or flush assets "forward" to some inventory or holding point. PA1 (c) Best Can Do (BCD). Given the current manufacturing condition and a prioritized set of demands which demands can be met in what time frame. BCD generally refers to a large set of demands. PA1 (1) Material Requirements Planning (MRP) type of matching--"Opportunity Identification" or "Wish list". For a given set of demand and a given asset profile, determine what work needs to be accomplished to meet demand. PA1 (2) Best Can Do (BCD) type of matching. Given the current manufacturing condition and a prioritized set of demands, determine which demands can be met in what time frame and establish a set of actions or guidelines to insure the delivery commitments are met in a timely fashion. BCD generally refers to large sets of demands. PA1 (3) Projected Supply Planning (PSP) type of matching. Given a set of assets, manufacturing specifications, and business guidelines what is the expected supply picture over the next "t" time units. PA1 (a) Capturing core production planning information from various legacy systems and storing them in a common format that is platform and solver independent. PA1 (b) A core set of "solvers" which match assets against demand in support of a variety of business planning processes, explain how the solution was obtained, and produce answers in a common format that is platform independent. The preferred embodiment has one major solver to support each type of the three types of matching. Within each solver the user has the ability to pick and choose between options and decision technologies. The solvers are referred to as AMRP (Advanced MRP), BCD (Best Can Do), and PSP (Projected Supply Planning). PA1 (c) A common format for storing results from running a solver that all solvers comply with. PA1 (d) Sending results of the solvers to other applications. PA1 (e) A work session manager or environment to enable users to easily use the various components of the planning software as appropriate. Examples of tasks the work session manager must handle are: (i) pulling a subset of the data, editing it, making sand box copies, analyzing the input data, BOM traces; (ii) selecting a solver, running the solver, saving the results, analyzing the solution (queries, reports, graphics, drill down); (iii) saving changed inputs or outputs to central location; and (iv) security of this work activity. PA1 (f) A batch job run facility.
Historically, these three types of matching have been viewed and practiced as distinct and unconnected processes and the solver tools used to support this need have each been unique to the particular type of matching. Furthermore solvers used in each of the tiers to perform similar function were also distinct and unconnected. For example, while tier 1 and tier 2 activities might both require an asset profile of what needs to be done to meet expected demand, the specific solvers used to perform this function were most likely to have been different among the tiers.
Arguably, the oldest type of matching is Material Requirements Planning (MRP). MRP is a system for translating demand for final products into specific raw material and manufacturing activity requirements by exploding demand backwards through the bill of material (BOM) and assets. Many authors have published papers and books on MRP. For example, Joseph Orlickly wrote Material Requirements Planning, published by McGraw-Hill, which has become a standard reference. As practiced in the microelectronics industry, MRP systems operate at a specific part number and inventory holding point level of detail.
The difficulty with traditional MRP was it did not provide an estimate about which demand would be met if insufficient resources were available and secondly how to prioritize manufacturing activity in light of insufficient resources. To fill this gap, two general types of tools were developed; (1) tools to examine the output of the MRP solution to help the user identify resource constraints and limited suggestions on how to alter demand, and (2) tools which attempt to create feasible and possibly optimal (or at least "good" or "intelligent") solutions to which demands can be met in what time frame. We will call this class of tool Best Can Do (BCD). In general, both types of BCD tools were provided to users as unconnected processes and tools. The second type of BCD tool often had no or very poor links to the MRP tool runs and often required aggregated data different in level of granularity from the MRP tool. The first type of BCD tool generated such large load levels on the user due to the limited intelligence of the these tools he or she was forced to move to aggregated data to avoid cognitive overload.
The third type of matching is projected supply planning (PSP). Typically, the user would attempt to create reasonable and feasible projected supply plans working with some level of aggregated data. The projected supply plans were then compared against aggregated demand statements to assess the quality of the fit. Dependent on desired presentation viewpoint, these projected supply planning (PSP) tools often used a level of aggregation of different granularity than the MRP. Sometimes such runs would be done with a level of aggregation above the MRP (for example, by using families of part numbers and weekly or monthly time buckets). At other times such runs would include additional detail beyond the standard MRP by planning work center level detail across the supply chain. In either case, results of the PSP were often difficult to link back to a subsequent MRP run. Most are done with electronic spreadsheets with only hand entered data. Some of the more advanced PSP tools were developed in the Application Programming Language (APL) during the early 1980s which provided a rudimentary bridging between the two competing levels of granularity.
Each type of the three types of matching assets with demand described above has its proper role in the world of manufacturing or production planning. Production planners and manufacturing managers responsible for matching have long understood that the three types of matching are not separate and distinct activities but different views of the same core problem. As such, this led to a need to interlink and bridge results from each of these types of matching which was often accomplished through ad hoc processes. Size and scope of the data made all but very limited procedures to link them impossible. Additionally, the need to bridge results from one tier to another was also clearly felt. Again, the size and scope of the data as well as the distinct solvers used by each of these tiers made bridging a difficult task. These difficulties promoted the understanding that business advantages could be gained from a tool which seamlessly supported the three types of matching in a synergistic manner and which could also facilitate the bridging of results from one tier to the next.